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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteAn AI agent should use memory for stable facts already supported by its context, search when information may be new or missing, and call a specialized tool for structured data, computation, execution, or an external action. After every call, it should inspect the result and stop as soon as the evidence is sufficient to answer reliably.
The choice between remembering, searching and using a tool is the central routing problem in an AI agent. A capable system does not retrieve everything, and it does not trust its internal knowledge for every question. It classifies the request, estimates whether its current evidence is enough, selects the smallest capable action, checks what came back and then decides whether another step is justified.
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The three routes an agent can take
Answer from memory and current context
Memory means the model’s learned knowledge plus information already present in the conversation, a supplied document or an approved internal context. This route fits claims that are stable, low risk and adequately supported. Explaining what a binary search tree is, rewriting a paragraph, or solving a well-specified algebra problem normally needs no web request.
“Memory” does not mean the model has perfect recall. It means that obtaining outside evidence would not materially improve the answer. If the prompt already includes the policy text to summarize, retrieving that same policy again adds latency without adding authority. The agent should still acknowledge uncertainty when the context is incomplete or contradictory.
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Search or retrieve information
Search is appropriate when the answer depends on freshness, a source the model has not been given, or a fact that can change. Anthropic’s web-search guidance specifically points to recent events, current prices or statistics, details about organizations and products that may have changed, and explicit requests to look something up. Retrieval can target the public web, a document store, a company wiki or another indexed collection.
Search is also the right response to a missing premise. If a user asks for the current return policy of a named retailer and the policy is not in context, the agent needs the retailer’s source rather than a plausible-sounding recollection. It should prefer an authoritative page, capture the relevant passage and preserve date or regional qualifiers.
Call a specialized tool
A tool is an operation the language model cannot safely or efficiently perform by speaking alone. APIs and databases provide structured records; calculators and code interpreters perform exact arithmetic; execution tools run tests or transform files; action tools send messages, create tickets or update systems. OpenAI’s Agents SDK describes hosted web search, file search, code interpreter, hosted MCP and image generation as examples of such capabilities.
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Tool use is not synonymous with search. A database query for an account balance, a calculator for compound interest and a calendar API for available appointment slots are tool calls even when no web page is involved. The agent should select a capability that matches the requested operation, not merely the most familiar integration.
How the routing decision is made
Five questions give an agent a practical decision procedure. They are qualitative judgments, not universal numeric thresholds.
| Question | What to inspect | Likely route |
|---|---|---|
| Does the fact change? | News, prices, schedules, availability, regulations, product specifications and other time-sensitive details | Search or a current API |
| Is the needed evidence already present? | Whether the prompt or trusted context contains the exact source and required scope | Memory/context if sufficient; retrieve if a gap remains |
| What kind of task is this? | Prose explanation versus lookup, calculation, execution or side effect | Memory for explanation; specialized tool for the latter operations |
| How costly is another round trip? | Latency, usage cost, rate limits and the user’s tolerance for delay | Use the smallest action that can resolve the uncertainty |
| Does the result meet the evidence standard? | Authority, relevance, completeness, consistency and required precision | Stop when sufficient; iterate only for a material gap |
The first two questions prevent stale or unsupported claims. The task question prevents using a text model where exact execution is required. Cost and evidence questions keep the loop efficient without allowing convenience to overrule reliability.
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When memory is the better answer
Stable concepts and transformations
Definitions, established mathematical methods, general programming concepts and creative transformations are usually stable. If a user asks what a REST endpoint is, requests a translation of supplied text or wants three headline options, a search call is noise. The agent can answer directly and spend its effort on clarity.
Information already supplied by the user
Context supplied in the prompt is often the most relevant source. A model can summarize a contract excerpt, compare two specifications pasted by the user or extract action items from meeting notes without retrieving duplicates. It should not silently fill a missing clause from memory; instead, it should identify what the provided material does not establish.
Low-risk answers with explicit uncertainty
For a low-consequence question where the model has partial but useful knowledge, a direct answer with a clear qualification may be preferable to a long retrieval process. The qualification must identify the uncertainty rather than imply current verification. High-stakes domains, however, raise the evidence requirement and commonly justify authoritative retrieval or a professional review.
When an agent should search
Freshness and change
Search when a date, version, price, statistic, office holder, service status, timetable or availability could have changed since the model’s knowledge was formed. “What is the current price?” and “Which version is supported now?” are lookup questions even if the agent remembers an older answer.
Named entities and policies
Specific organizations, products and regulations need source-level verification. Retrieve the named organization’s documentation or an official register rather than relying on a generic description. Keep geography, edition, account tier and effective date attached to the result; a true statement for one region can be wrong for another.
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If the user says “search the web,” “check the latest,” “look up the filing” or supplies a URL to inspect, that instruction is itself a routing signal. The agent should honor it, while still choosing an appropriate source and stopping once the requested question is answered.
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Missing or conflicting context
A retrieval call is warranted when the prompt omits a fact on which the answer depends, or when two supplied sources disagree and a primary source can resolve the conflict. The agent should state the conflict if it remains unresolved instead of averaging incompatible claims.
How retrieval differs from a tool call
Retrieval returns evidence for the model to read; a function or service tool performs a defined operation. Web search, file search and document retrieval answer “what does a source say?” A calculator answers “what is the exact result?” A flight API answers “which records currently match these constraints?” An email tool answers “can the requested message be sent?”
| Request | Best capability | Reason |
|---|---|---|
| Latest central-bank rate | Search an official source or query a market API | The value is current and source-dependent |
| Compute a repayment schedule | Calculator or code interpreter | Exact arithmetic and repeatable output matter |
| Find inventory for a SKU | Inventory database/API | Structured, account- or location-specific data is required |
| Explain recursion | Memory | The concept is stable and no external fact is needed |
| Book an appointment | Calendar or booking action tool | The task has an external side effect |
A search result may inform a later action, but the agent should keep the stages distinct: retrieve the policy, verify eligibility, then call the booking or update function with constrained parameters. This separation makes permissions and auditing easier.
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What agentic RAG adds to retrieval
Traditional fixed retrieval-augmented generation retrieves documents at a predetermined point. Microsoft describes the agentic alternative this way: “Instead of a fixed pipeline, an AI agent treats retrieval as a tool that it can invoke on demand.” AWS defines it as a retrieval-augmented pattern in which the agent decides when to retrieve, what to retrieve, which retrieval tool to use and whether the returned context is sufficient before proceeding.
In practice, the agent first interprets the request, chooses a query or source, examines the returned passages and decides whether they cover the question. It can reformulate a query, switch from a general search to a domain index or request a second source when a material uncertainty remains. It is not required to retrieve on every turn, and it should not continue merely because another call is available.
A reliable multi-step tool-selection loop
- Parse the goal and constraints. Identify the deliverable, time scope, geography, permissions, precision and any explicit request to search or act.
- Check existing evidence. Look in the conversation and approved context for the exact facts needed. Mark each important premise as supported, missing, stale-risk or conflicting.
- Choose the smallest capable operation. Use a direct answer for stable prose, a focused retrieval query for missing evidence, or the narrowest API, calculator or execution tool that can perform the operation.
- Constrain inputs. Pass specific dates, identifiers, units, account scopes and result limits. Avoid broad queries that return irrelevant material or expose unnecessary data.
- Inspect the result. Check source authority, freshness, relevance, completeness, units, permissions and contradictions. Treat an empty, truncated or malformed response as a failed step, not as evidence.
- Decide whether another call is justified. Continue only when a known gap could change the answer or action. Otherwise synthesize the answer with the applicable qualifications.
- Record provenance and side effects. For consequential answers, retain source names, timestamps and tool outputs; for actions, confirm what was changed and report any user-visible identifier.
Cohere’s documented multi-step pattern supports this staged approach: a model can plan which tools to use, in what order and with which parameters. Planning does not remove the need to validate each intermediate result.
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When should an agent stop searching?
There is no authoritative universal number of searches or a percentage confidence threshold. The correct stopping rule is evidence sufficiency for the user’s stated need. Stop when an authoritative, relevant result answers every material part of the question, its date and scope fit the request, and remaining uncertainty would not change the conclusion.
- The source directly addresses the requested entity, period and region.
- Key claims are supported by a primary source or a clearly appropriate authoritative record.
- Numbers, units, versions and conditions are internally consistent.
- Any unresolved ambiguity is disclosed and does not alter the recommended answer.
- An additional call would add duplication, marginal context or latency rather than resolve a defined gap.
Continue searching when the only result is secondary or stale, when sources conflict on a decision-critical fact, when a query returned partial coverage, or when the user asked for a comparison that requires multiple named items. If no reliable source resolves the issue, say so and explain what is known instead of presenting repeated weak results as certainty.
Worked routing examples
“What is the latest price of this laptop?”
The word “latest” and a changing commercial value trigger retrieval. Search the manufacturer’s or specified retailer’s current listing, verify currency, configuration, region and whether the figure is promotional, then stop when the requested model and market are covered. Memory of an older launch price is not sufficient.
“Explain how a hash table handles collisions.”
This is a stable educational concept. Answer from memory, use a small example and mention that implementations differ. A web call would not improve a general explanation unless the user asks about a particular library’s current behavior.
“Calculate tax on these invoices.”
Use a calculator or code tool after confirming jurisdiction, tax rate, rounding rule and units. Retrieval may be needed for the current rate, but arithmetic should be executed rather than estimated in prose. Show inputs and outputs so the user can audit the result.
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First retrieve the applicable policy if it is not supplied. Then verify the user’s account and eligibility through an authorized account tool. Only after confirmation should the agent call the cancellation action, report the effective date and preserve the confirmation. Reading a policy alone cannot perform the side effect.
“Compare the current support windows for products A and B.”
This is a multi-item, freshness-sensitive request. Retrieve current official lifecycle pages for both products, normalize definitions such as security support versus feature support, note publication or access dates and identify any remaining disagreement. A single search result for one product is incomplete evidence.
Common routing failures and fixes
Searching stable questions
Unnecessary retrieval increases latency and can introduce low-quality snippets or contradictory wording. Classify the task first and answer directly when the supplied context is enough.
Trusting memory for changing facts
Models can produce a fluent but obsolete price, release date or policy. Add freshness triggers for dates, “current” language, named products and explicit lookup requests, and require a source timestamp in the final answer.
Using search for computation or actions
A web page is not a calculator, database transaction or booking system. Route exact arithmetic to executable computation and route side effects to permissioned functions with confirmation and idempotency safeguards.
Calling every available tool
Tool abundance can become a search loop. Require the planner to name the uncertainty a next call will resolve, constrain the call, inspect its output and stop when that uncertainty is gone.
Accepting the first plausible result
Relevance ranking is not proof. Check the primary source, publication date, jurisdiction, version and completeness. When sources disagree, surface the disagreement and explain which source governs the decision.
Designing the policy in an agent
Implement routing as explicit policy rather than an undocumented intuition. A concise internal rule can be expressed as: if stable_and_supported: answer; else if current_or_missing: retrieve; else if structured_or_executable_or_action: call_specialized_tool; inspect; repeat_only_for_material_gap. In production, pair that rule with tool schemas, permission checks, timeouts, rate limits, source allowlists and logs.
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The result is a simple but disciplined behavior: use memory when it is enough, retrieve when reality or evidence may have moved, use a specialized capability when language alone cannot perform the task, and end the loop when further evidence would not change the answer.
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